An Improved Human Action Recognition Method Based on 3D Convolutional Neural Network

Abstract

Aiming at the problems such as complex feature extraction, low recognition rate and low robustness in the traditional human action recognition algorithms, an improved 3D convolutional neural network method for human action recognition is proposed. The network only uses grayscale images and the number of image frames as input. At the same time, two layers of nonlinear convolutional layers are added to the problem of less convolution and convolution kernels in the original network, which not only increases the number of convolution kernels in the network. Quantity, and make the network have better abstraction ability, at the same time in order to prevent the network from appearing the phenomenon of overfitting, the dropout technology was added in the network to regularize. Experiments were performed on the UCF101 data set, achieving an accuracy of 96%. Experimental results show that the improved 3D convolutional neural network model has a higher recognition accuracy in human action recognition.